The Perceiver IO architecture, [1]}, was developed with the goal of achieving a DL scheme that can easily integrate and transform arbitrary information for arbitrary tasks. This architecture employs an attention encoder that maps inputs from a wide range of modalities to a fixed-size latent space using cross-attention, this latent space is then further processed using self-attention as an usual Transformer and decoded into the output domain via cross-attention. This process allows the network to scale to large and multi-modal data since it decouples the bulk of the network’s processing from the size and modality-specific details of the input. In this work, we will leverage this property and use Perceiver IO to learn non-linear deformations on unstructured meshes without adding any information or restrictions about how to treat the underlying data structure. During training, Perceiver IO automatically learns the important dependencies that exist in the input domain, composed of arbitrarily unstructured mesh data, and transforms them into the corresponding output which consists of displacement data.
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